1. Technical Field
The present invention relates generally to the field of autonomous classification, and more specifically to enabling creation of autonomous classifiers.
2. Description of Related Art
Prior art exists in classification systems (R. O. Duda, P. E. Hart, and D. G. Stork. “Pattern Classification.” Wiley, New York, 2nd edition, 2000, hereby incorporated by reference). Most classification systems go through 2 phases. The training of the classifier is conducted in supervised or semi-supervised fashion, where human input is required to train the mapping function from the input space to the decision space. In the classification phase, the classification system is presented with an input data. The classifier then uses its mapping function to produce a decision corresponding to the input. Prior art also exists in the area of autonomous management of computing infrastructure. The classifier is involved in the process of sample selection during training in a class of systems referred to as “active learners.” (Naphade, Lin, Tseng, Smith and Basu, “Learning to Annotate Video Databases”, SPIE Storage and Retrieval for Media Databases, January 2002, hereby incorporated by reference). In this work the authors show how the process of learning the mapping function from the input data to the decision space can be efficiently learnt if the classifier is involved in the process of sample selection.
Prior Art in the area of autonomous computing exists at the level of autonomous management of computing infrastructure as the IBM eLiza autonomic computing project which aims for self-configuring, self-healing, self-optimizing and self-protecting computing infrastructure (available on IBM's Website regarding autonomic servers); interoperability amongst low level web services such as the WSDL, or the high-level semantic interoperability for web services such as the Semantic Web (J. Handler, T Berners-Lee and E. Miller, “Integrating Applications on the Semantic Web”, Journal of the Institute of Electrical Engineers of Japan, Vol 122(10), October 2002, p. 676-680, hereby incorporated by reference). The main idea of the semantic web is to extend the current world wide web in which information is given well-defined meaning, thus better enabling computers and people to work in cooperation. The main idea is to have data on the Web defined and linked such that it can be used for more effective discovery, automation, integration and reuse across various applications. The aim is to provide an infrastructure that enables services, sensors, programs and appliances to both consume and produce data on the web. New protocols and languages are being developed rapidly to standardize the ways in which systems describe what they do. An XML-based protocol called SOAP (available on the Web) has been developed to provide standard means for allowing programs to invoke other programs on the web. In addition new web service description and web service languages are emerging. While predefined service definitions may be easier to handle, discovering new services that use different descriptions may not be possible without newly emerging resources such as the web ontology language (available on the Web). There is no existing system or framework currently that actually allows autonomous classification systems.